Robust regression methods for intensive care monitoring

Robust methods for heteroskedastic regression

Heteroskedastic regression data are modelled using a parameterized variance function. This procedure is robustified using a method with high breakdown point and high efficiency, which provides a direct link between observations and the weights used in model fitting. This feature is vital for the application, the analysis of international trade data from the European Union. Heteroskedasticity is...

Robust outcome prediction for intensive-care patients.

Missing data are a major plague of medical databases in general, and of Intensive Care Unit databases in particular. The time pressure of work in an Intensive Care Unit pushes the physicians to omit randomly or selectively record data. These different omission strategies give rise to different patterns of missing data and the recommended approach of completing the database using median imputati...

Robust regression methods for real-time PCR

Robust Methods for Partial Least Squares Regression

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variab...

ELECTRE: Robust ordinal regression for outranking methods

Article history: Received 25 October 2010 Accepted 31 March 2011 Available online 8 April 2011